Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2013
Abstract
Illegal cyberspace activities are increasing rapidly and many software engineers are using reverse engineering methods to respond to attacks. The security-sensitive nature of these tasks, such as the understanding of malware or the decryption of encrypted content, brings unique challenges to reverse engineering: work has to be done offline, files can rarely be shared, time pressure is immense, and there is a lack of tool and process support for capturing and sharing the knowledge obtained while trying to understand assembly code. To help us gain an understanding of this reverse engineering work, we conducted an exploratory study at a government research and development organization to explore their work processes, tools, and artifacts [1]. We have been using these findings to improve visualization and collaboration features in assembly reverse engineering tools. In this talk, we will present a review of the findings from our study, and present prototypes we have developed to improve capturing and sharing knowledge while analyzing security concerns.
Keywords
malware, reverse engineering, empirical study
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 7th International Conference on Foundations of Augmented Cognition, Held as Part of HCI International 2013, Las Vegas, July 21-26
First Page
113
Last Page
122
ISBN
9783642394539
Identifier
10.1007/978-3-642-39454-6_12
Publisher
SpringerLink
City or Country
Verlag, Berlin
Citation
CLEARY, Brendan; TREUDE, Christoph; FIGUEIRA FILHO, Fernando; STOREY, Margaret-Anne; and SALOIS, Martin.
Improving tool support for software reverse engineering in a security context. (2013). Proceedings of the 7th International Conference on Foundations of Augmented Cognition, Held as Part of HCI International 2013, Las Vegas, July 21-26. 113-122.
Available at: https://ink.library.smu.edu.sg/sis_research/8950
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-642-39454-6_12